Using animal blood, the researchers hope to improve the accuracy of AI driven diagnostic technology

What does a cheetah, a tortoise, and a Humboldt penguin have
in common? They are zoo animals helping scientists at Saarland University in
Saarbrücken, Germany, find biomarkers that can help computer-assisted diagnoses
of diseases in humans at early stages. And they are not the only animals
lending a paw or claw.

In their initial research, the scientists used blood samples
that had been collected during routine examinations of 21 zoo animals between
2016 and 2018, said a news
release. The team of bioinformatics
and human genetics experts
worked with German zoos Saarbrücken and Neunkircher for the study. The project
progresses, and thus far, they’ve studied the blood of 40 zoo animals, the
release states.

This research work may eventually add useful biomarkers and
assays that clinical
laboratories can use to support physicians as they diagnose patients,
select appropriate therapies, and monitor the progress of their patients. As medical
laboratory scientists know, for many decades, the animal kingdom has been
the source of useful insights and biological materials that have been
incorporated into laboratory assays.

“Measuring the molecular blood profiles of animals has never
been done before this way,” said Andreas
Keller, PhD, Saarland University Bioinformatics Professor and Chair for
Clinical Bioinformatics, in the news release. The Saarland researchers published
their findings in Nucleic Acids
Research, an Oxford
Academic journal.

“Studies on sncRNAs [small non-coding RNAs] are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens,” the article states.

However, the researchers perceived an inability for AI and machine learning to
discern real biomarker patterns from those that just seemed to fit.

“The machine learning methods recognize the typical
patterns, for example for a lung tumor or Alzheimer’s disease. However, it is
difficult for artificial intelligence to learn which biomarker patterns are
real and which only seem to fit the respective clinical picture. This is where
the blood samples of the animals come into play,” Keller states in the news
release.

“If a biomarker is evolutionarily conserved, i.e. also
occurs in other species in similar form and function, it is much more likely
that it is a resilient biomarker,” Keller explained. “The new findings are now
being incorporated into our computer models and will help us to identify the
correct biomarkers even more precisely in the future.”

“Because blood can be obtained in a standardized manner and
miRNA expression patterns are technically very stable, it is easy to accurately
compare expression between different animal species. In particular, dried blood
spots or microsampling devices appear to be well suited as containers for
miRNAs,” the researchers wrote in Nucleic Acids Research.

Additionally, human volunteers contributed blood specimens
for a total of 19 species studied. The scientists reported success in capturing
data from all of the species. They are integrating the information into their
computer models and have developed a public database of their
findings for future research.

“With our study, we provide a large collection of small RNA
NGS expression data of species that have not been analyzed before in great
detail. We created a comprehensive publicly available online resource for
researchers in the field to facilitate the assessment of evolutionarily
conserved small RNA sequences,” the researchers wrote in their paper.

Clinical Laboratory Research and Zoos: A Future
Partnership?

This novel involvement of zoo animals in research aimed at improving
the ability of AI driven diagnostics to isolate and identify human disease is
notable and worth watching. It is obviously pioneering work and needs much
additional research. At the same time, these findings give evidence that there
is useful information to be extracted from a wide range of unlikely sources—in
this case, zoo animals.

Also, the use of artificial intelligence to search for
useful patterns in the data is a notable part of what these researchers
discovered. It is also notable that this research is focused on sequencing DNA
and RNA of the animals involved with the goal of identifying sequences that are
common across several species, thus demonstrating the common, important
functions they serve.

In coming years, those clinical laboratories doing genetic
testing in support of patient care may be incorporating some of this research
group’s findings into their interpretation of certain gene sequences.